Assignment #2 for Computer Networks

Transcription

1 Assignment # for Computer Networks Savvas C. Nikiforou Department of Computer Science and Engineering University of South Florida Tampa, FL 6 Abstract The purpose of this assignment is to compare the queueing behavior of real network traffic input to an infinite size queue versus the M/M/. To simulate the real network a trace file was used, which was taken on the USF backbone - Mbps Ethernet connected to the router to the Internet. The results of the experiment show the difference between the real network simulation and the M/M/ simulation, which grow bigger as the server utilization increases. The difference in the results between the two simulations is due to the assumption that the network traffic has a Poisson arrival time as well as packet length. It is clearly shown that it is not really the case but instead the packet size as well as the arrival time histograms is showing a very bursty behavior. Another thing that it is not taken into account for the M/M/ model is the self-similarity of the data. I. INTRODUCTION The purpose of this paper is to calculate how closely the behavior of a real network is to that of the more theoretical M/M/. In order to model network traffic most of the time many assumptions are usually made. This is necessary because computer networks are unpredictable. It is very important though to be able to predict the behavior of the traffic in a network because this will help to maximize utilization of resources in the network. An assumption that is made very often is that packet sizes and arrival times are Poisson processes. This type of processes is predictable and easy to work with, but they don t always describe the network under testing very precisely. Recent work shows that LAN traffic is much better mo deled using statistically self-similar processes because they have much different theoretical properties than Poisson processes []. For self-similar traffic, there is no natural length for a burst ; traffic bursts appear on a wide range of time scales []. Another way to predict packet arrivals in a computer network is to use techniques similar to that predicting memory paged references in a paged memory computer []. It is observed that page references are correlated such that the probability of a page being referenced decreases as the time to its previous reference increases. Similarly if we find that the probabilities of packets going to different destinations in a computer network are not the same then we may use different strategies than if we assume the probabilities to be the same []. II. Previous Work Jain in [] suggests a new model for computer network traffic and argues that a packet train model is more close to the traffic in a real network than the Poisson distribution assumed in other models such as the M/M/ queue. A histogram of the packet arrival times was used to show that the traffic does not have a Poisson distribution. For the traffic to be Poisson the histogram has to be exponential. The one of the real network though it was not exponential and at times it had lots of bursts. The measurements on real traffic led to a new model of arrival, which was named the train model. It suggests that packets flowing very close to each other most probably they have the same destination as the first packet in the bunch. This is similar to a train where all the cars have the same destination as the locomotive that is pulling them. Larger gap in time than a pre specified maximum suggests a different packet train with a destination different than the previous train. The main reason that the M/M/ model is not very similar to real networks is because of the actual real traffic characteristics, which do not obey the Poisson distribution. [] Paxson et al discusses why the failure of the Poisson model in the wide area networks. The M/M/ Model: III. BACKGROUND The M/M/ model is a single server model with an infinite size queue. M stands for a Markovian and the first M is referred to the Arrival rate whereas the second refers to the Service Distribution. The system has a single server with an infinite capacity of a queue and the number of possible customers is unlimited. The M/M/ queue displays exponential service time. In the M/M/ model the probability of an arrival is independent of the previous one. i.e the traffic is independent. A steady state is reached when the number of arriving customers is less than rate at which the server can provide service to them. The queue discipline is first come first served. The M/M/ queue id described the following set of formulae.

2 L q = ρ /( - ρ), where L q = length of the queue W q = (λ/µ ) / ( - ρ) where W q = wait in queue IV. THE SETUP FOR THE SIMULATION Packet Size Histogram To compare the behavior of the M/M/ and real network several simulation programs were used. Values were gathered for both the real network and the M/M/ for different utilization values varying from to 8%. In order to gather results for different utilization values the link speed (or media rate) was varied for both simulation programs. The histogram of the inter arrival times is shown in Figure. As it can be seen from the histogram the time distribution is not a Poisson process. To be such the logarithmic histogram must be a straight line. Instead what we observe here is that the Histogram has a lot of bursts and lots of high values at small time stamps. At higher time stamps it has many bursts, which is quite different from the theoretical Poisson arrival, which is a straight line. This affects the results of the simulation a great deal. Frequency Arrival Time Histogram Time in µs Figure. Inter arrival time histogram For the simulations the CSIM libraries and simulation tools were used. The CSIM program is available from Mesquite Software. It is a simulation tool that is used to simulate artificial traffic based on the values of arrival time and packet length. In Figure the packet size histogram is presented where again we see that the distribution is not Poisson and it has lots of bursts as in the case of the time arrivals. For the simulations in this paper the incoming traffic was used. This is important to be mentioned because having used the outgoing traffic the results might be different. This again happens because the traffic coming into a closed network is not necessarily the same as the traffic leaving the network. Frequency Packet Size in bytes Figure. Packet Size Histogram V RESULTS Real Network Simulation M/M/ Results?(% ) Q L T R T S Q L T R T S Table Simulation Results Running the simulation for the real network as well as the M/M/ we gathered results on mean service time, response time utilization and queue length. The results for the two simulations are summarized in Table, where? is the utilization; Q L is the mean queue length; TR is the mean response time; and TS is the mean service time.

3 We can see that the response time increases as the utilization increases and the same is true for the mean queue length. Another observation is that the results for the real network are really close to those of M/M/ for low utilization but as the utilization goes up so does the difference in the values, especially for the queue length. On the other hand the service time does not change dramatically as it is expected but although. Graph represents the delay Vs the utilization for the two simulations. We can see that M/M/ performs slightly better for small utilization than the real network, but as the utilization increases the real network has a better Delay Vs Utilization Time in ms Utilization (%) Graph. Response time Vs Utilization Trace M/M/ Delay (Queue Length) 6 8 Utilization (%) Graph Delay vs Utilization Trace M/M/ performance. The change in performance takes place little after the % utilization. It is interesting to see the difference in percentile delay for the two systems. These results are presented in Table and Graph shows the difference graphically. The point where the values are changing from positive to negative is where the quality of performance changes for the two systems. This is the same place where the two curves meet in Graph one.?(%) Trace M/M/ Difference Delay Delay (%) Table The difference in Delay In graph the response time for the two models is plotted against the utilization. It is obvious that the performance of the theoretical M/M/ model is superior to that of the real network. This is because of the irregularity of the time arrivals in the real network. In order to achieve different utilization values the link speed was varied. Then the value used for the speed for each utilization value was also used to calculate the packet delay, by dividing the packet size in bits by the link speed in Mbps. This was giving the delay time in seconds. Then it was easy to calculate the mean variance and % values of the delay times by simply running one of the tools on Dr. Christensen s Tool s page. As it can be seen from the results the mean delay increases as the utilization increases. The Results are shown in Table. Mean delay variance and Standard Deviation are displayed in Table. As it can be seen from the results all the values increase as the Utilization increases. This means that as the server becomes more busy the delay of the packets in the queue increases with it. The values are in µs. This is expected in a way because as the server utilization increases the jobs that have to be performed by the server are more therefore more system handling is also needed from the server site. As the Utilization approaches % the mean delay time increases dramatically. Utilization % %

4 8.. Table Mean Delay % service time in order to increase utilization but usually this may not be feasible with real networks. Difference (Trace - M/M/) ? (%) Mean Variance Std Dev Table Mean, Variance and s Difference in Deay Utilization Graph Trace M/M/ Vs Utilization VII. CONSIDERING PACKET LOSS AS A RESPONSE VARIABLE (Extra credit part) If we are to consider packet loss as a response variable then we have to decide how to deal with the queue size. We can approach the problem in two ways, either by limiting the number of customers that can be in the queue or by limiting the byte capacity of the queue and allowing any number of customers which their packet sum does not exceed the byte size of the queue. Each method has pros and cons. The best solution depends mostly on the traffic characteristics. If the mean packet size is very small then allowing for the byte size queue instead of the customer number makes more sense since in this way we can accommodate more customers. On the other hand if the mean packet size is large we might want to limit the number of the customers. In this case we have to make sure that the capacity of the queue will be large enough to accommodate N number (where N the number of customers allowed in the queue) times the maximum possible packet length that a packet can have. This though makes us rethink the solution of N customers queue. The reason for that is that we may not know the maximum size that a packet can have. Also in the case where many small packets need to be queued we want allow it wasting in this way bandwidth. The best solution is to limit the queue buffer size by bytes and not by packet number. VI. CONCLUSIONS The behavior of the real network is similar to the M/M/ for some values of utilization whereas it varies a lot for other. This is because of the real traffic characteristics. In the case of the M/M/ model we assume that the arrival packet time as well as the packet size as both independent from the previous arrivals. In other words we assume Poisson distribution arrival time and packet length. This does not happen though with real data. If we take real data for a very long time then their distribution becomes closer to Poisson but they have a lot of bursts, which makes the real networks have a different behavior from the theoretical M/M/ model. The variable of interest to achieve better performance is actually the Media Rate or Link speed. We can also vary the ACKNOWLEDGEMENTS I would like to thank Dr. Christensen for Answering questions concerning the assignment. Also the T.A. of the class Mr. Hiroshi for his valuable help and availability to answer questions any time. Several hours of discussion on how to approach the solution to this assignment took place with my colleague Nikhil Kelshikar as well as other students in the class. REFERENCES [] Vern Paxson and Sally Ford Wide-Area Traffic: The failure of Poisson Modeling [] Will E. Leland, Walter Willinger, Murrad S. Taqqu and Daniel V.Wilson. On the Self -Similar Nature of Ethernet Traffic [] Raj Jain and Shawn Routhier. Packet Trains Measurements and a New Model for Computer Network Traffic.

31 CHAPTER 3 CALL CENTER QUEUING MODEL WITH LOGNORMAL SERVICE TIME DISTRIBUTION 3.1 INTRODUCTION In this chapter, construction of queuing model with non-exponential service time distribution, performance

Queuing Theory Queuing Theory Queuing theory is the mathematics of waiting lines. It is extremely useful in predicting and evaluating system performance. Queuing theory has been used for operations research.

White Paper Best Practices in Core Network Capacity Planning Architectural Principles of the MATE Portfolio of Products What You Will Learn Core network capacity planning is the process of ensuring that

CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

LECTURE - 1 INTRODUCTION TO QUEUING SYSTEM Learning objective To introduce features of queuing system 9.1 Queue or Waiting lines Customers waiting to get service from server are represented by queue and

Network traffic: Scaling 1 Ways of representing a time series Timeseries Timeseries: information in time domain 2 Ways of representing a time series Timeseries FFT Timeseries: information in time domain

TCP PERFORMANCE IN MOBILE-IP Foo Chun Choong Department of Electrical Engineering, National University of Singapore ABSTRACT The throughput performance of TCP in Mobile-IP [1] was investigated. Compared

Proceedings of the 4 IEEE United States Military Academy, West Point, NY - June Defending Against Traffic Analysis Attacks with Link Padding for Bursty Traffics Wei Yan, Student Member, IEEE, and Edwin

Exponential Growth and Modeling Is it Really a Small World After All? I. ASSESSSMENT TASK OVERVIEW & PURPOSE: Students will apply their knowledge of functions and regressions to compare the U.S. population

ALOHA Class of Multiple Access Protocols ALOHA, also called pure ALOHA: Whenever a user has a frame to send, it simply transmits the frame. If collision occurs, it waits for a random period of time and

Density Curve A density curve is the graph of a continuous probability distribution. It must satisfy the following properties: 1. The total area under the curve must equal 1. 2. Every point on the curve

10-3 Measures of Central Tendency and Variation So far, we have discussed some graphical methods of data description. Now, we will investigate how statements of central tendency and variation can be used.

Time Series Analysis of Network Traffic Cyriac James IIT MADRAS February 9, 211 Cyriac James (IIT MADRAS) February 9, 211 1 / 31 Outline of the presentation Background Motivation for the Work Network Trace

The Systems Approach to Problem Solving I. Introduction This course introduces the methodology systems engineers use to solve problems. You will learn about many concepts and tools that systems engineers

Statistics for Engineers 4-1 4. Introduction to Statistics Descriptive Statistics Types of data A variate or random variable is a quantity or attribute whose value may vary from one unit of investigation

Congestion Control Abusayeed Saifullah CS 5600 Computer Networks 1 Network Conges-on Conges-on: When one part of the subnet (e.g. one or more routers in an area) is overloaded. The network and transport

AN 10.12 Performance Analysis and Software Optimization on Systems Using the LAN91C111 1 Introduction This application note describes one approach to analyzing the performance of a LAN91C111 implementation

The problem with waiting time Why the only way to real optimization of any process requires discrete event simulation Bill Nordgren, MS CIM, FlexSim Software Products Over the years there have been many

ECON 2A: Advanced Macroeconomic Theory I ve put together some pointers when assembling the data analysis portion of your presentation and final paper. These are not all inclusive, but are things to keep

Introduction to Metropolitan Area Networks and Wide Area Networks Chapter 9 Learning Objectives After reading this chapter, you should be able to: Distinguish local area networks, metropolitan area networks,

Voice over IP Demonstration 1: VoIP Protocols Network Environment We use two Windows workstations from the production network, both with OpenPhone application (figure 1). The OpenH.323 project has developed

3. MONITORING AND TESTING THE ETHERNET NETWORK 3.1 Introduction The following parameters are covered by the Ethernet performance metrics: Latency (delay) the amount of time required for a frame to travel

Chapter 1 Review Questions R1. What is the difference between a host and an end system? List several different types of end systems. Is a Web server an end system? 1. There is no difference. Throughout

Corvil Insight Low-Latency Market Data of 12 Contents How much bandwidth do you really need? 4 How to ask the bandwidth question for low-latency? 4 What about loss? 5 Exchange feed example 5 Working with

A New Adaptive FEC Loss Control Algorithm for Voice Over IP Applications Chinmay Padhye and Kenneth J. Christensen Computer Science and Engineering University of South Florida Tampa, FL 336 {padhye, christen}@csee.usf.edu

Modelling and Simulation of Voice over Internet Protocol (VOIP) Idogho Philipa, Idigo V.E, Agajo James Abstract Real time voice transmission is now widely used over the Internet and has become a very significant

Internet Management and Measurements Measurements Ramin Sadre, Aiko Pras Design and Analysis of Communication Systems Group University of Twente, 2010 Measurements What is being measured? Why do you measure?

To ensure the functioning of the site, we use cookies. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy &amp Terms.
Your consent to our cookies if you continue to use this website.